Description

Book Synopsis

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.



Table of Contents
Computing on Edge Devices in Internet-of-things (IoT).- The Rise of Machine Learning in IoT system.- Least-squares-solver for Shadow Neural Network.- Tensor-solver for Deep Neural Network.- Distributed-solver for Networked Neural Network.- Conclusion.

Compact and Fast Machine Learning Accelerator for

    Product form

    A Hardback by Hantao Huang, Hao Yu

    £98.99

    Includes FREE delivery

    RRP £109.99 – you save £11.00 (10%)

    Order before 4pm tomorrow for delivery by Wed 21 Oct 2026.

    1 in stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Compact and Fast Machine Learning Accelerator for by Hantao Huang

      Book details

      Publisher Springer Verlag, Singapore
      Published 18 December 2018
      ISBN-13 9789811333224
      978-9811333224
      ISBN-10 981133322X
      Also in:
      Artificial intelligence (AI) Computer architecture and logic design Optimization

      Description

      Book Synopsis

      This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.



      Table of Contents
      Computing on Edge Devices in Internet-of-things (IoT).- The Rise of Machine Learning in IoT system.- Least-squares-solver for Shadow Neural Network.- Tensor-solver for Deep Neural Network.- Distributed-solver for Networked Neural Network.- Conclusion.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account